Prerequisites
- Basic Math Knowledge: High-school-level algebra and statistics are desirable.
- Computer Science Fundamentals: Familiarity with variables, functions, loops, and data structures like lists and dictionaries.
- Programming Skills: A foundational understanding of coding is recommended.
Course outline
- 1
Lesson 1: Foundations of Artificial Intelligence
- 1.1 Introduction to AI
- 1.2 Types of Artificial Intelligence
- 1.3 Branches of Artificial Intelligence
- 1.4 Applications and Business Use Cases
- 2
Lesson 2: Mathematical Concepts for AI
- 2.1 Linear Algebra
- 2.2 Calculus
- 2.3 Probability and Statistics
- 2.4 Discrete Mathematics
- 3
Lesson 3: Python for Developer
- 3.1 Python Fundamentals
- 3.2 Python Libraries
- 4
Lesson 4: Mastering Machine Learning
- 4.1 Introduction to Machine Learning
- 4.2 Supervised Machine Learning Algorithms
- 4.3 Unsupervised Machine Learning Algorithms
- 4.4 Model Evaluation and Selection
- 5
Lesson 5: Deep Learning
- 5.1 Neural Networks
- 5.2 Improving Model Performance
- 5.3 Hands-on: Evaluating and Optimizing AI Models
- 6
Lesson 6: Computer Vision
- 6.1 Image Processing Basics
- 6.2 Object Detection
- 6.3 Image Segmentation
- 6.4 Generative Adversarial Networks (GANs)
- 7
Lesson 7: Natural Language Processing
- 7.1 Text Preprocessing and Representation
- 7.2 Text Classification
- 7.3 Named Entity Recognition (NER)
- 7.4 Question Answering (QA)
- 8
Lesson 8: Reinforcement Learning
- 8.1 Introduction to Reinforcement Learning
- 8.2 Q-Learning and Deep Q-Networks (DQNs)
- 8.3 Policy Gradient Methods
- 9
Lesson 9: Cloud Computing in AI Development
- 9.1 Cloud Computing for AI
- 9.2 Cloud-Based Machine Learning Services
- 10
Lesson 10: Large Language Models
- 10.1 Understanding LLMs
- 10.2 Text Generation and Translation
- 10.3 Question Answering and Knowledge Extraction
- 11
Lesson 11: Cutting-Edge AI Research
- 11.1 Neuro-Symbolic AI
- 11.2 Explainable AI (XAI)
- 11.3 Federated Learning
- 11.4 Meta-Learning and Few-Shot Learning
- 12
Lesson 12: AI Communication and Documentation
- 12.1 Communicating AI Projects
- 12.2 Documenting AI Systems
- 12.3 Ethical Considerations
Materials
All necessary course materials are included.
System requirements
Minimum technical expectations for the online learning environment. Your IT team can use this as a checklist.
Internet connectivity
Cable, Fiber, DSL, or LEO Satellite (i.e. Starlink) internet with speeds of at least 10mb/sec download and 5mb/sec upload are recommended for the best experience.
While cellular hotspots may allow access to our courses, users may experience connectivity issues by trying to access our learning management system. This is due to the potential high download and upload latency of cellular connections. Therefore, it is not recommended that students use a cellular hotspot as their primary way of accessing their courses.
Hardware
- CPU: 1 GHz or higher
- RAM: 4 GB or higher
- Resolution: 1280 x 720 or higher. 1920x1080 resolution is recommended for the best experience.
Speakers / Headphones Microphone for Webinar or Live Online sessions.
Operating system
Windows 7 or higher. Mac OSX 10 or higher. Latest Chrome OS Latest Linux Distributions.
While we understand that our courses can be viewed on Android and iPhone devices, we do not recommend the use of these devices for our courses. The size of these devices do not provide a good learning environment for students taking online or live online based courses.
Web browser
Latest Google Chrome is recommended for the best experience. Latest Mozilla FireFox Latest Microsoft Edge Latest Apple Safari.
Recommended software
Office suite software (Microsoft Office, OpenOffice, or LibreOffice) PDF reader program (Adobe Reader, FoxIt) Courses may require other software that is described in the above course outline.
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